The use of inertial navigation control systems in aircraft is very conventional at the present time. These control systems use accelerometers for determining accelerations along axes defined relative to the aircraft, gyroscopes for determining angular rotation velocities relative to axes that are also defined with respect to the aircraft, and optionally other sensors, such as a barometric altimeter. By integrating the gyroscopic measurements, the orientation of the aircraft at a given moment is determined. By integrating the accelerometer measurements, which may be normalized with respect to an earth reference frame external to the aircraft, thanks to knowledge of the orientation of the aircraft, the velocity components of the aircraft in this Earth reference frame are determined. By integrating the velocities, the geographical positions are determined.
However, measurement sensors are imperfect and have intrinsic measurement errors or bias, which may also vary over the course of the navigation—these are then referred to as short-term errors—or which may vary over long periods (over a year or some ten years)—these errors are then referred to as long-term errors. Bias is more problematic when the position calculations made on the basis of sensor measurement results involve integrations. Integration generates a drift in the measured value, which drift progressively increases over the course of time whenever the integrated value is biased at the start. A double integration (acceleration integral in order to give the velocity and then velocity integral to give the position) further increases this drift considerably.
The short-term errors of the sensors are corrected by means of a recalibration, as for example in the patent FR 2 830 320, in which an inertial navigation control system is hybridized with at least one satellite positioning receiver. The correction may also be made thanks to a numerical model using an often “real time” primary measurement of a quantity, the perturbing effect of which on the measurement is known, for example the internal temperature of the sensors.
The long-term errors cannot be reduced by these methods as they are linked to the stability of the sensor over a time of quite long duration, possibly several tens of years. To a first order, the long-term errors are linked to the stability of the bias (or offset) and to the stability of the sensitivity (or scale factor) of the sensor.
One method of reducing the long-term errors of a measurement sensor consists in constructing the measurement sensor from extremely stable components. The stability of the component, or of the material on which the measurement is based, for example a mechanical component, an electrical component or a gas, guarantees stability of the measurement over time. This very natural method may prove to be extremely expensive as it dictates the use of top-of-the-range components.